{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f73fb235",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hello world\n"
     ]
    }
   ],
   "source": [
    "print(\"hello world\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1d22860c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Package                       Version\n",
      "----------------------------- -----------\n",
      "aiofiles                      22.1.0\n",
      "aiosqlite                     0.18.0\n",
      "anaconda-client               1.12.0\n",
      "anaconda-navigator            2.4.2\n",
      "anyio                         3.5.0\n",
      "argon2-cffi                   21.3.0\n",
      "argon2-cffi-bindings          21.2.0\n",
      "asttokens                     2.0.5\n",
      "attrs                         22.1.0\n",
      "Babel                         2.11.0\n",
      "backcall                      0.2.0\n",
      "backports.functools-lru-cache 1.6.4\n",
      "backports.tempfile            1.0\n",
      "backports.weakref             1.0.post1\n",
      "beautifulsoup4                4.12.2\n",
      "bleach                        4.1.0\n",
      "boltons                       23.0.0\n",
      "brotlipy                      0.7.0\n",
      "certifi                       2023.7.22\n",
      "cffi                          1.15.1\n",
      "chardet                       3.0.4\n",
      "charset-normalizer            2.0.4\n",
      "click                         8.0.4\n",
      "clyent                        1.2.2\n",
      "colorama                      0.4.6\n",
      "comm                          0.1.2\n",
      "conda                         23.7.2\n",
      "conda-build                   3.26.0\n",
      "conda-content-trust           0+unknown\n",
      "conda_index                   0.2.3\n",
      "conda-package-handling        2.2.0\n",
      "conda_package_streaming       0.9.0\n",
      "conda-repo-cli                1.0.41\n",
      "conda-token                   0.4.0\n",
      "conda-verify                  3.4.2\n",
      "contourpy                     1.1.0\n",
      "cryptography                  41.0.2\n",
      "cycler                        0.11.0\n",
      "debugpy                       1.6.7\n",
      "decorator                     5.1.1\n",
      "defusedxml                    0.7.1\n",
      "entrypoints                   0.4\n",
      "executing                     0.8.3\n",
      "fastjsonschema                2.16.2\n",
      "filelock                      3.9.0\n",
      "fonttools                     4.42.0\n",
      "future                        0.18.3\n",
      "glob2                         0.7\n",
      "graphviz                      0.8.4\n",
      "idna                          2.6\n",
      "importlib-metadata            6.0.0\n",
      "importlib-resources           5.2.0\n",
      "ipykernel                     6.25.0\n",
      "ipython                       8.12.0\n",
      "ipython-genutils              0.2.0\n",
      "jedi                          0.18.1\n",
      "Jinja2                        3.1.2\n",
      "json5                         0.9.6\n",
      "jsonpatch                     1.32\n",
      "jsonpointer                   2.1\n",
      "jsonschema                    4.17.3\n",
      "jupyter_client                7.4.9\n",
      "jupyter_core                  5.3.0\n",
      "jupyter-events                0.6.3\n",
      "jupyter-server                1.23.4\n",
      "jupyter_server_fileid         0.9.0\n",
      "jupyter_server_ydoc           0.8.0\n",
      "jupyter-ydoc                  0.2.4\n",
      "jupyterlab                    3.6.3\n",
      "jupyterlab-pygments           0.1.2\n",
      "jupyterlab_server             2.22.0\n",
      "kiwisolver                    1.4.4\n",
      "libarchive-c                  2.9\n",
      "lxml                          4.9.2\n",
      "MarkupSafe                    2.1.1\n",
      "matplotlib                    3.7.2\n",
      "matplotlib-inline             0.1.6\n",
      "menuinst                      1.4.19\n",
      "mistune                       0.8.4\n",
      "more-itertools                8.12.0\n",
      "mxnet                         1.7.0.post2\n",
      "navigator-updater             0.4.0\n",
      "nbclassic                     0.5.5\n",
      "nbclient                      0.5.13\n",
      "nbconvert                     6.5.4\n",
      "nbformat                      5.7.0\n",
      "nest-asyncio                  1.5.6\n",
      "notebook                      6.5.4\n",
      "notebook_shim                 0.2.2\n",
      "numpy                         1.24.4\n",
      "packaging                     23.0\n",
      "pandocfilters                 1.5.0\n",
      "parso                         0.8.3\n",
      "pathlib                       1.0.1\n",
      "pickleshare                   0.7.5\n",
      "Pillow                        9.4.0\n",
      "pip                           23.2.1\n",
      "pkginfo                       1.9.6\n",
      "pkgutil_resolve_name          1.3.10\n",
      "platformdirs                  2.5.2\n",
      "pluggy                        1.0.0\n",
      "ply                           3.11\n",
      "prometheus-client             0.14.1\n",
      "prompt-toolkit                3.0.36\n",
      "psutil                        5.9.0\n",
      "pure-eval                     0.2.2\n",
      "pycosat                       0.6.4\n",
      "pycparser                     2.21\n",
      "Pygments                      2.15.1\n",
      "PyJWT                         2.4.0\n",
      "pyOpenSSL                     23.2.0\n",
      "pyparsing                     3.0.9\n",
      "PyQt5                         5.15.7\n",
      "PyQt5-sip                     12.11.0\n",
      "pyrsistent                    0.18.0\n",
      "PySocks                       1.7.1\n",
      "python-dateutil               2.8.2\n",
      "python-dotenv                 1.0.0\n",
      "python-json-logger            2.0.7\n",
      "pytz                          2022.7\n",
      "pywin32                       305.1\n",
      "pywinpty                      2.0.10\n",
      "PyYAML                        6.0\n",
      "pyzmq                         23.2.0\n",
      "QtPy                          2.2.0\n",
      "requests                      2.28.1\n",
      "requests-toolbelt             1.0.0\n",
      "rfc3339-validator             0.1.4\n",
      "rfc3986-validator             0.1.1\n",
      "ruamel.yaml                   0.17.21\n",
      "ruamel.yaml.clib              0.2.6\n",
      "Send2Trash                    1.8.0\n",
      "setuptools                    68.0.0\n",
      "sip                           6.6.2\n",
      "six                           1.16.0\n",
      "sniffio                       1.2.0\n",
      "soupsieve                     2.4\n",
      "stack-data                    0.2.0\n",
      "terminado                     0.17.1\n",
      "tinycss2                      1.2.1\n",
      "toml                          0.10.2\n",
      "tomli                         2.0.1\n",
      "toolz                         0.12.0\n",
      "tornado                       6.3.2\n",
      "tqdm                          4.65.0\n",
      "traitlets                     5.7.1\n",
      "typing_extensions             4.7.1\n",
      "ujson                         5.4.0\n",
      "urllib3                       1.22\n",
      "wcwidth                       0.2.5\n",
      "webencodings                  0.5.1\n",
      "websocket-client              0.58.0\n",
      "wheel                         0.38.4\n",
      "win-inet-pton                 1.1.0\n",
      "y-py                          0.5.9\n",
      "ypy-websocket                 0.8.2\n",
      "zipp                          3.11.0\n",
      "zstandard                     0.19.0\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip list"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee5dc78f",
   "metadata": {},
   "source": [
    "这是一元二次方程求解公式\n",
    "$$x = \\frac{-b\\pm \\sqrt{b^2-4ac}}{2a}$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "725199c8",
   "metadata": {},
   "source": [
    "这是爱因斯坦的质能转换方程，揭示了质量和能量之间的关系\n",
    "$E =mc^2$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "322f9c5b",
   "metadata": {},
   "outputs": [],
   "source": [
    "pip install matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "51307c5c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "x = np.arange(0,10,0.01)\n",
    "y1 = 2 * x\n",
    "y2 = 5 * x \n",
    "y3 = x ** 2\n",
    "plt.plot(x,y1,'r',x,y2,'g',x,y3,'b')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "86d561d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "x = np.arange(0,5,0.01)\n",
    "y1 = np.sin(x)\n",
    "y2 = np.cos(x)\n",
    "plt.plot(x,y1,'r',x,y2,'g')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d38eb9d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "sys.path.append('/your/local/python/path')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d8bd00b6",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "ceshi-py3.8",
   "language": "python",
   "name": "ceshi-py3.8"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
